Abstract
This paper describes an e-commerce application build on the Electronic Trading Opportunities System. This system enables ‘Trade Points’ and trade related bodies to exchange information by e-mail. This environment offers an enormous trade potential and opportunities to small and medium enterprises, but its efficiency is limited since the amount of circulating messages surpasses the human limit to analyze them. The application described here aids this process of analysis, allowing the extraction of the most relevant characteristics from the messages. The application is structured in three phases. The first is responsible for analyzing and for providing structural information about texts. The second identifies relevant information on texts through clustering and categorization processes. The third applies Information Extraction techniques, which are aided by the use of a domain specific knowledge base, to transform the unstructured information into a structured one. By the end, the user gets more quality in the analysis and can more easily find interesting ideas, trends and details, creating new trade opportunities to small and medium enterprises.
This research is partially sponsored by grants from CNPq and CAPES.
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Scarinci, R.G., Wives, L.K., Loh, S., Zabenedetti, C., de Oliveira, J.P.M. (2003). Managing Unstructured E-Commerce Information. In: Olivé, A., Yoshikawa, M., Yu, E.S.K. (eds) Advanced Conceptual Modeling Techniques. ER 2002. Lecture Notes in Computer Science, vol 2784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45275-1_36
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DOI: https://doi.org/10.1007/978-3-540-45275-1_36
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